Animated distribution of Nudibranchs with {gifski}

Mapping the distribution of a species is a useful way to understand a how environmental factors influence its range over time. One useful way to see changes over time is by using animation to view multiple distributions in succession. Here we will model the distribution of Nudibranchia (Nudibranchs) across Australia and create an animated visual of its yearly distribution with the {gifski} package.

Eukaryota
Animalia
Mollusca
Maps
Authors

Stephanie Woolley

Olivia Torresan

Dax Kellie

Published

February 26, 2022

Author

Stephanie Woolley
Olivia Torresan
Dax Kellie

Date

26 February 2023

Species distribution models (SDMs) are used to predict the habitat range of different organisms. They are useful as they can show us how changing environmental conditions affect how probable it is that a species occurs in a specific location. Knowing more about where species are likely to occur is useful for managing ecosystems, conserving species and predicting expected effects of climate change.

Species rarely stay in the same spot for long periods of time. Just like us, they react to changes in their environment and interactions with other species and with other individuals of their own species. As a result, it can be more useful to see how a distribution of a species changes over time. Particularly in marine environments where seemingly small changes in temperature, light or chemical composition can result in large changes in species distributions.

Here we will use a conservative SDM to predict the distribution of Nudibranchs around Australia, mapping monthly to look at seasonal changes. This post is inspired by Liam Bailey’s cool (and hilarious) Bigfoot distribution map and code which can be found here

SDM basics

Before we begin, it is important to first understand the basics behind SDMs. SDMs are built using different types of statistical models depending on the type and suitability of your data. However, in every SDM there are always 2 main inputs:

  1. Occurrence data
  2. Environmental variables

From this, the model predicts the probability of a species being found across a broader area. It takes the locations the species has already been found, then finds the associated environmental variables, and uses this to assess the suitability of the remaining area.

Having gone over the basics, let’s begin building our SDM of Nudibranchs across Australia.

Download and prepare data

First load the necessary packages.

library(dplyr)     # Data wrangling 
library(galah)     # Download observations
library(stars)     # Convert raster to easier format 
library(ozmaps)    # Australian map
library(SSDM)      # Linear modelling
library(sdmpredictors) # Environmental variables 
library(grDevices) # Colours and fonts
library(ggplot2)   # Map plotting 
library(maps)      # Cities for map
Expand for session info
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.2 (2022-10-31 ucrt)
 os       Windows 10 x64 (build 19044)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  English_Australia.utf8
 ctype    English_Australia.utf8
 tz       Australia/Sydney
 date     2023-02-20
 pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version date (UTC) lib source
 abind         * 1.4-5   2016-07-21 [1] CRAN (R 4.2.0)
 dplyr         * 1.1.0   2023-01-29 [1] CRAN (R 4.2.2)
 galah         * 1.5.1   2023-01-13 [1] CRAN (R 4.2.2)
 ggplot2       * 3.3.6   2022-05-03 [1] CRAN (R 4.2.1)
 htmltools     * 0.5.4   2022-12-07 [1] CRAN (R 4.2.2)
 maps          * 3.4.0   2021-09-25 [1] CRAN (R 4.2.1)
 ozmaps        * 0.4.5   2021-08-03 [1] CRAN (R 4.2.1)
 sdmpredictors * 0.2.13  2022-09-13 [1] CRAN (R 4.2.1)
 sessioninfo   * 1.2.2   2021-12-06 [1] CRAN (R 4.2.1)
 sf            * 1.0-8   2022-07-14 [1] CRAN (R 4.2.1)
 SSDM          * 0.2.8   2020-02-28 [1] CRAN (R 4.2.1)
 stars         * 0.5-6   2022-07-21 [1] CRAN (R 4.2.1)

 [1] C:/Users/KEL329/R-packages
 [2] C:/Users/KEL329/AppData/Local/Programs/R/R-4.2.2/library

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